1. Field of the Invention
The invention relates in general to computer-memory systems and in particular to data-transfer operations and data-transfer architectures for such systems. The invention has special relevance for both information retrieval and the expert-systems branch of artificial intelligence.
It is to be noted, however, that while the subject invention will be described with reference to particularized end uses, the invention is not limited to such uses. Those having ordinary skill in the art and access to the teachings of this specification wil recognize additional utilizations within the invention's scope.
2. Description of the Prior Art
A. Basic Associational Requirement
In certain classes of data-accessing systems, a central functional requirement for operational effectiveness is the ability to establish associations between related data items. Consider, for example, the field of information retrieval. Given an inputted information query whose ultimate objective is the retrieval of an appropriate stored item of information, the system in effect must be able to establish an association between the query and the stored item in order for that item to be selected as a retrieval output. Similarly, an expert system in the field of artificial intelligence, when presented with a given decisional problem, must likewise be able to establish an association between the elements of the problem and the appropriate ones of its stored decision rules in order for the problem to be dealt with in a correct manner.
A central deficiency of the prior-art systems has been their net operational inefficiency in the formation of such associations. Among the factors which have been commonly symptomatic of this associational inefficiency are first, the necessity for a high level of skill on the part of the system operator; second, the difficult and labor-intensive nature of the design task required to produce such systems; and third, the inordinate complexity of the systems themselves. The causal factors for these disadvantageous consequences will be examined following the next-presented overview of the basic nature of some of the principal prior-art data-accessing systems.
B. Basic Operational Situations
1. Information Retrieval
The fundamental task of a typical information-retrieval system is to access within memory an appropriate portion of a stored textual data base. In a first type of retrieval system, the data base, usually in the form of ordinary sequential-language text, is accompanied by an inverted file comprising an alphabetical listing of the more-meaningful items within the original text. Included along with a given item in the alphabetized list is a software "pointer" identifying that item's location in the original text.
An information query into such a system commonly takes the form of a set of key words connected by logical operators. For example, given a stored text whose contents was the topic "Weather" and given that the retrieval task was the accessing of those portions of the text dealing with the use of barometric pressure in predicting the likelihood of rain, a typical query might include the key words "barometric pressure," and "probability of precipitation," while the logical operator might be an "AND." The query would then appear as "barometric pressure AND probability of precipitation." The system would process this query by examining the contents of its alphabetized listing for the presence of the key words and then moving to the associated pointer-indicated portions of the original text where the original language would then be examined for the conjunctive presence of both sets of key words.
In a second typical type of retrieval system, the search for correspondences between the query and the data base is performed by hardware mechanisms instead of the described inverted software file. The usual hardware implementation of this nature processes the information query simply by performing a sequential, item-by-item check of the entire contents of the original stored text for the concurrence of the subject query elements.
In contrast with these first two types of systems which ultimately require that a given set of words in the textual data be "anticipated" by a query encompassing basically those very same words, a third type of prior-art retrieval system supplementally employs a form of thesarus. Textual retrieval from a system of this nature occurs when words in the data base match either the query words themselves or their thesarus-stored synonyms. While in associational terms the first two system types thus depend on identicalities between the query and the data base, the thesarus supplies a measure of less-exact, secondary-associational capability.
2. Artificial Intelligence
A basic task in the field of artificial intelligence is the searching of those representations of human knowledge known as semantic networks. These networks typically take the form of an interconnected array of nodes. The nodes represent individual items of information, while the interconnections represent the relationships between these information items. In the specialized situation of what is known as an expert system whose ultimate functional task is the independent management of an operational problem such as the automatic diagnosis of malfunctions in an electronic system, the basic nodal items of information would be the elemental decision rules needed for an appropriate diagnosis of a given operational difficulty. A system of this nature thus more-specifically operates by searching its semantic network for the appropriate decision rule needed for the resolution of the subject external problem.
Such systems have typically been implemented through some combination of software and hardware techniques. The basic software approach has been to create an extensive file whose individual data items are the nodal elements of semantic information. Included in the stored file along with the basic information items are software pointers referencing, and thus establishing the necessary interconnections with, the appropriate related nodal elements. Alternatively, hardware mechanizations for the nodal network may employ hardware processing elements as the nodes, with the relations between nodes being established by means of physically-wired interconnections between the processing elements. From the standpoint of overall network architecture, the nodes and interconnections have typically been configured so that the resulting composite array takes the form of either a branched hierarchy or a rectangular grid. A search of such networks commonly takes the form of an "interrogation" which propagates in parallel throughout the interconnected nodal array in an attempt to locate the appropriate stored decision rule. With a branched hierarchy, the propagation is "binary" in that there are typically two relationally-interconnective alternatives flowing from informational decisions made at any given node. A four-way propagation is analogously possible in response to the decisions made at the nodes of a typical grid-configured network.
C. Consequential Difficulties
1. Information-Retrieval Systems
With reference to the initially-presented comments concerning the central functional necessity of being able to establish associations between related data items, it may be observed that the typically-configured prior information-retrieval systems possess only the most-elementary types of associational capabilities of their own. These capabilities are effectively limited to simply the detection of exact correspondences between the inputted query elements and the stored textual data base. The presentation of a query which is highly anticipatory of the data-base contents is thus crucial to retrieval effectiveness.
A consequential major disadvantage of the prior systems is that the formulation of appropriately-anticipatory queries of this nature is a task requiring an often-considerable degree of background knowledge and innate skill. The systems are thus largely dependent upon the inputs of a highly-skilled user whose knowledgeable initial insights effectively serve as intermediate associations between a motivating informational need and the minimal, exact-detection capabilities of the typical retrieval system. It is thus apparent that the prior systems are more-appropriately characterizable primarily as user-associated systems in which the operator must be relied upon to effectively supply needed associations.
Another difficulty with the prior systems is that certain aspects of their makeup often require labor-intensive efforts during system establishment. For example, even though the above-discussed secondary-associational capability of a supplemental thesarus can enable the operator-skill requirements to be relaxed somewhat, the generation of such a thesarus is a time-consuming task ultimately requiring hand-coded associations. Systems of this nature are thus supplementally characterizable as designer-associated by virtue of synonymically-anticipated associations supplied on a pre-use basis.
The problem of output interpretation is yet another deficiency of the prior systems which makes them still-further dependent upon the user's skill level. The central difficulty here is that the typical system's output, in those cases where the precise informaiton desired has not been retrieved initially, does not itself effectively provide meaningful suggestions as to alternative avenues of query. Knowledgeable insights are typically again required in both analyzing what output is available and then synthesizing further queries. It may additionally be noted that this output problem is typically a compounding of the skill-dependent query-formulation problem in that overly-simplistic queries often give rise to inordinate amounts of output information, while overly-simplistic queries produce output which is uninformatively insufficient.
A final noteworthy problem concerns the often-impracticable operational complexity of the systems themselves. For example, the alphabetized inverted file is operationally cumbersome to merely maintain and even more cumbersome to update when additional entries must be inserted in conjunction with augmentations of the textual data base. It is furthermore apparent that the more extensive the data base, the greater the resulting complexity of the overall multi-file system. This previously-unavoidable complexity has inherently carried the potential of precluding the application of existing, automated-retrieval techniques to truly large-scale information-retrieval problems.
2. Expert Systems
Although the designer-associated nature of the typical expert system lessens the degree to which reliance must be placed on intelligent direction by a human operator, it is apparent that the design task itself is one of considerable formidability. A semantic network is an artificial construct whose nature and characteristics must be analyzed in advance and then tediously mechanized in order to achieve system operationality. It is conjunctively apparent that the greater the complexity of the operational problem to which it is desired to apply the capabilities of the expert system, the greater the complexity of the resulting system itself. Often inherently associated with an increase in complexity is an increase in inefficiency, as for example the commonly-inadequate operational speed of serially-processing computers. Complex inefficiency of this nature again often operates prospectively to preclude the construction of expert systems for real-world tasks to which such systems are otherwise theoretically applicable.
It may also be noted that an equally-complex aspect of the prior systems has been the nature of the network-alteration task required when operational modifications to a given system become desired. Such modifications are typically accomplished only by means of a tedious reworking of the overall nodal array. Extensive reprogramming and even intricate physical alterations are commonly-unavoidable aspects of such reworking.
D. Fundamental Unsatisfied Need
The shortcomings of the prior art have given rise to a basic need for a data-accessing system which would have a readily-mechanized self-associational capability with a significantly-reduced dependency on externally-supplied associations, and which would consequentially provide high net-operational efficiency while requiring neither highly-skilled users, nor labor-intensive design efforts nor an inordinate degree of intrinsic system complexity.